SIR-3DCNN: A Framework of Multivariate Time Series Classification for Lung Cancer Detection

Electronic noses (e-noses) have proven effective in detecting lung cancer by analyzing Volatile Organic Compounds (VOCs) in breath samples, with Multivariate Time Series Classification (MTSC) as the primary task. However, challenges remain in effectively capturing spatiotemporal information for MTSC. To address this, a novel method called SIR-3DCNN for MTSC in lung cancer detection is introduced. A pivotal aspect of SIR-3DCNN is the Sensor Array Optimization (SAO), an algorithm based on Linear Discriminant Analysis (LDA) that can decrease the number of sensors from 22 to 8 while increasing accuracy by 2.35 percentage points in our study. Furthermore, SIR-3DCNN incorporates an advanced technique for representing spatiotemporal information, converting optimized Multivariate Time Series (MTS) into Maximum Trajectory Matrix Images (MTMIs) and arranging them to maximize the Sum of Inter-Frame Mutual Information (SIFMI). Additionally, we have developed C3D-Light, a lightweight yet effective 3D Convolutional Neural Network (3DCNN) that demonstrates superior performance compared to other models. Comparative analyses with state-of-the-art methodologies reveal that SIR-3DCNN consistently outperforms existing methods, achieving perfect sensitivity (100%), the highest specificity (80%), accuracy (92.94%), AUC (94.85%), precision (90.26%), and F1-score (94.86%) among all compared methods. This advancement holds significant promise for lung cancer detection using electronic noses. The source code is available at https://github.com/cqu-3dteam/sir-3dcnn.